Apr 17, 2007 - with, the sample may constitute a more selected group, health-wise, than comparable. Americans.16. In a previous version of the paper, we had ...
PRELIMINARY: PLEASE DO NOT CITE
Do Health Shocks Increase Retirement More When Workers are Universally Insured? By Nabanita Datta Gupta1 Kristin J. Kleinjans2 and Mona Larsen3 17 April 2007
JEL Codes: I12, I18, J26 Abstract: Health shocks should increase retirement to a larger extent in a health-care system with universal insurance compared to an employment-contingent health insurance system without retiree insurance. To investigate this, we compare the effect of a new acute condition or new chronic disease on the probability of working for pay on sub-samples of workers aged 51-64 in the Danish Longitudinal Registers, 1991-2001, and similarly defined sub-samples drawn from the US HRS, 1992-2002. The results show that while new chronic conditions lead individuals to reduce work at about the same rate in both countries, older Americans are three times as likely to stop working for pay following an acute condition than similar Danes. When taking into account insurance constraints, this gap does narrow by almost two-thirds for single women, largely because single women whose employer plan does not provide retiree insurance tend to work more after an acute health shock. Insurance constraints, however, fail to reduce the country differences for any of the other groups we study. We test several other potential explanations for the differential work response following acute health shocks across the two countries. ___________________________________________________________ 1
The Danish National Institute of Social Research,Visiting Scholar, NBER (Aging) and IZA, Bonn. University of Aarhus and RAND. 3 The Danish National Institute of Social Research. We thank Malene Kallestrup for very competent research assistance and Kristina Bacher Svendsen, MD Phd, for expert advice on the interpretation and aggregation of the ICD8/ICD10 diagnosis codes. We are grateful to our discussant Arie Kapteyn, Daniel Hamermesh, Michael Hurd, Susann Rohwedder and other participants of the IZA workshop on the "The Well-Being of the Elderly”, May 2006, our discussant Thomas Crossley at the Danish National Institute’s annual Advisory Research Board Workshop, October 2006 and our discussants Arthur Van Soest and Gwenaël Piaser and other participants at the Netspar Workshop in Utrecht, November 2006 for their valuable comments on a previous version of the paper. 2
I. Introduction Health shocks significantly alter workers’ retirement plans, in fact more so than a worsening in an already existing health condition. Dwyer and Hu (2000) find that developing a new work limitation between two waves of the US Health and Retirement Survey (HRS) increases the likelihood of retirement more than having a persistent limitation. Coile (2004) finds that the shock effect of an unexpected health event such as a heart attack or the onset of a new chronic condition leads to a serious financial loss for the family mainly due to the reduced labour supply of the affected spouse, since added worker effects are small. Further, Coile and Milligan (2005) give evidence that such health shocks lead to a decline in business ownership and portfolio reduction. The labour supply response following a health shock, however, would depend on having access to pensions and health insurance. Within an employment-contingent health insurance system without retiree health insurance, workers may be forced to continue working simply to be able to pay the costs of their medical treatment. For example, Bradley et al. (2005) find that among a sample of American married women, those who develop breast cancer are more likely to continue working and even increase the intensity of labour supply following the onset of the disease. Gruber and Madrian (1996) exploit variation in US state and federal 'continuation of coverage' COBRA mandates (laws allowing individuals to continue purchasing health insurance through a former employer for a given period of time after leaving the firm) to determine the impact of health insurance on retirement. They find that one year of continuation benefits increases the baseline probability of being retired by 5.4% for males aged 5564. That is, giving older employees continued health insurance coverage reduces their need to work to cover the large costs of unexpected medical expenses. Rust and Phelan
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(1997) estimate a stochastic dynamic programming model of labor supply and Social Security receipt and find that for “health insurance constrained” individuals–those with no form of health insurance other than Medicare and who can only obtain fairly priced health insurance through their employer’s group plan–the combination of risk aversion and the highly skewed nature of health care costs (small expenses occur frequently, but also catastrophic events do occur, although rarely) creates a high security value of remaining in employment until Medicare eligibility at age 65. Thus, an incomplete market for health insurance in part explains the peak in retirement occurring at age 65. Similarly, Blau and Gilleskie (2001) find that availability of employer-provided retiree health insurance (EPHRI) increases the exit rate from employment by 6 percentage points if the firm pays the entire cost. Yet, other studies turn up small effects of health insurance on retirement behavior, for example Lumsdaine, Stock and Wise (1992) which was one of the first studies to investigate the effects of employer provided health insurance on retirement outcomes within a structural model. Still, as their study pertained to only a single firm, retirement in their framework meant cessation of employment at the firm rather than exit from the labor market. Gustman and Steinmeier (1994) also estimate a structural model, but on a nationally representative longitudinal dataset, the RHS 1969-1979, and reproduce a rather small effect of retiree health coverage on retirement, i.e., an acceleration of retirement age by less than one month compared to a base of no retiree health coverage. More recently, Blau and Gilleskie (2006) estimate a structural dynamic model of the employment behavior of older married couples, including risky medical expenditures and health insurance, and find that health insurance only has
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modest impacts on the labor force exit behavior of couples1. They conclude it is unlikely that health insurance affects labor force behavior via the budget constraint. Their model lacks a savings decision, but a structural dynamic model by French and Jones (2004) which includes both savings and health insurance and is estimated on data from the HRS also suggests that Medicare only has a modest effect on retirement behavior. Job exit rates at age 65 are found to be only slightly higher for individuals whose health insurance is tied to their job than those whose health insurance is not tied to their jobs. While the dynamic structural models above can form the basis for policy evaluation, which reduced form models cannot, there are at the current moment at least three shortcomings even in the most advanced of these models: (i) these models are calibrated to somewhat crudely defined health changes based on self-reports rather than actual medical events2; (ii) the models do not allow for unobserved individual heterogeneity; and (iii) the policy conclusions arising from these models are based on a comparison of individuals with health insurance coverage to individuals without, and do not take into account that health insurance is to some extent chosen by individuals and therefore endogenous.3 In this paper we explore the effect of health shocks on retirement by estimating simple reduced-form panel retirement models investigating the effect on the probability of retirement of new health disturbances which occur within the sample period. We 1
Blau and Gilleskie (2006) simulate the impact of policy reforms such as universal health insurance (giving everyone health insurance independent of employment status) and find only small effects on the employment status of married couples. They also raise the age of Medicare eligibility from 65 to 67 and find negligible effects on employment. 2 Rust and Phelan (1997) measure health status at time t as either 1=good health, 2=bad health or 3=dead; Blau and Gilleskie (2006) use a dichotomous measure of good (combining excellent, very good and good) and bad (combining poor and fair) health. 3 One exception is a study by Card, Dobkin and Maestas (2004) who use increases in health insurance coverage at age 65 to generate exogenous variation in health insurance which they relate to health care utilization and health behaviors.
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draw data from two sources: First, sub-samples of older singles and married persons from the Danish Longitudinal Registers 1991-2001, and second, matched sub-samples from the US HRS, 1992-2002. This comparison is meaningful as the two countries enjoy nearly the same level of prosperity in terms of GDP per capita in PPP’s, the same life expectancy, similar levels and trends in the ageing of the population, about the same average age of retirement, and very similar rates of unused productive capacity in the age group 55-64 (Gruber and Wise, 1998; Economic Council of Denmark, 1998). Yet, institutions contrast sharply, with income tax-financed universal health insurance, nationalized health care and generous social protection in Denmark, compared to the more liberal American economic system. Our aim, therefore, is to compare the retirement effects of negative health shocks occurring in a universally insured health care system to those found in similar US sub-samples, both with and without insurance coverage, for narrowly defined health conditions. When workers are universally insured, insurance is not contingent on employment. Therefore, we would expect that health shocks would increase retirement to a larger extent in a general tax-financed health-care system with universal access such as the Danish one, compared to the US. The results from our study can be expected to inform policy makers in countries considering the adoption of universal health insurance schemes and concerned with the possible output loss due to increased early retirement as a consequence. Since our focus is on the work response following a health shock and we adopt a reduced form approach, we are not constrained by state space considerations or the number of controls, and hence we are able to employ somewhat better quality health data in this paper than in previous studies. That is, we observe the occurrence of new
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acute or chronic conditions that are expected to impose serious work limitations, such as heart attack, stroke, or new cancer. In fact, changes in self-reported health correlate weakly with contemporaneous actual medical events4 (the correlation being 0.09 with a new acute condition and 0.15 with a new chronic condition, see Appendix Table A.1. based on the US HRS, where positive correlations mean that self-reported health worsens). It is an empirical question whether self-reported health changes or actual health events are more salient for the retirement decision. We do not explore that question here, but merely note that the subjective measures seem to capture a different aspect of health than the diagnostic measures, and a useful exercise could be to see whether using objectively measured health gives stronger predictions regarding the impact of health insurance on retirement following a health shock than what has been found in previous studies. The longitudinal nature of our datasets and the panel methods employed in this paper allow us to account for unobserved individual effects unlike the dynamic structural models. Taking into account unobserved individual heterogeneity in those models would, however, most likely strengthen the conclusion that health insurance has a minor effect on retirement behavior. Finally, while previous studies have drawn policy conclusions by comparing individuals with insurance to those without based on fairly limited samples sizes in settings where health insurance is a choice variable, we bring more robust evidence to the question of the retirement response following a health shock in the presence of health insurance by drawing data from the longitudinal population registers of a country where health insurance is universal.
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They correlate also weakly a period before or a period after the health shock.
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Our main result shows, surprisingly, that health shocks have less of an impact on paid work in Denmark than for similarly-defined samples drawn from the US HRS applying the same controls. In fact, while older individuals 51-64 in Denmark are about 6-7.5 pct. points (8-10% of baseline probability) less likely to continue working for pay following a new acute condition, the same age group is 17-23 pct. points (21-28% of baseline probability) less likely to do so in the US, even when matching on observables. Interestingly, the labor supply response following the diagnosis of a new chronic condition is more similar, associated with a 6-12 pct. points (8-16% of baseline) lower probability of working for pay in Denmark, and 8-18 pct. points (10-22% of baseline) in the US. We test several potential explanations behind this country difference in impacts. The rest of the paper is organized as follows: Section II lays out the theoretical foundation for the analyses, Section III discusses the data, Section IV presents in brief the estimation method, Section V the results, Section VI discusses other potential explanations, and Section VII concludes.
II. Theoretical background In this section, we present a simple theory of how health shocks influence the retirement decision through the budget constraint. For an individual approaching retirement age, the problem is to choose retirement age, r, maximizing the sum of the discounted per period utilities of working from age t to age r-1 and retiring at age r and the utilities of retiring thereafter until T, the highest possible age.
r −1
T
S =t
S =r
V (r ) = ∑ β S −t U w (C S , H S ) + ∑ β S −tU R (C S (r ),0)
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(1)
The instantaneous utility of work is UW , which depends on consumption and hours worked (bad) and the utility of retirement UR, which depends only on consumption, as workers are assumed to stop working after retirement.
The constraints are given by: C S = wH S + AS where A is asset income, and w the wage rate, and C S (r ) = BS (r ) + AS where B(r) are retirement benefits which depend on the age of retirement. Thus, retirement at r+1 is preferred to retirement at r if V(r+1) > V(r). This occurs if
U W (C r , H r ) > U R (C r (r ),0) ,
(2)
i.e., current utility is higher if working than if retired, and, in particular, if this firstorder effect is sufficiently large in magnitude to dominate the second-order effect
T
∑β
S = r +1
S −t
U R (C S (r + 1),0) − U R (C S (r ),0)) ,
(3)
which would vanish in a regime where benefits do not depend on age of retirement, but is likely negative.5 If the first-order effect dominates the second-order effect (under riskneutrality simply the accrual effect), the retirement date is the first date r such that
U w (C r , H r ) < U R (C r (r ),0) .
(4)
5
As most benefits systems are not actuarially fair, benefit accrual -- the increase in expected present discounted value of future social security benefits if retirement is postponed by a year-- is typically negative (Gruber and Wise, Social Security Programs and Retirement Around the World – Microestimation, University of Chicago Press, 2004).
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Now suppose the worker suffers a health shock at time r. If hours do not adjust, then consumption while working is reduced to C s' = wH S + AS − M S where MS is medical expenses, and consumption in retirement becomes C s' (r ) = B S (r ) + AS − M S .
So, retirement should be delayed if the reduction in utility is less if working, i.e. if
∂U W ∂U R (C r , H r ) < (C r (r ),0) ∂C r ∂C r
(5)
As Cr(r) < Cr , typically, this would be the case. That is, (4) would be obtained by the increase in leisure in retirement at the expense of a drop in consumption, and with decreasing marginal utility.6 All in all, it is quite likely a health shock defers retirement when workers must pay out-of-pocket the costs of their medical treatment. On the other hand, with access to universal insurance, clearly a health shock would induce the individual to retire earlier if health was explicitly introduced in U(.), making inequality (4) more likely.
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α
An example is U (C S , H S ) = αC S + f ( H S ) , Gustman and Steinmeier (1994) i.e. U is additively
separable in CS and HS, and α < 1, with decreasing marginal utility. In this case, clearly (4) is satisfied, α −1
i.e. C r
< C r (r ) α −1 since α-1 < 0. Even if C r (r ) ≥ C r and also for other utility functions the result α
may still go through. For example consider U (C S , H S ) = αC S
(24 − H S ) β , the standard log-linear α −1
(24 − H r ) β < C r (r ) α −1 24 β β β and since ( 24 − H r ) < 24 this is satisfied even for many Cr (r ) ≥ C .
utility of consumption and leisure. Here, in order for (4) to hold, C r
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III. Data and descriptives
The first dataset in this paper is obtained from a huge Danish longitudinal register database that includes yearly information on all persons aged over 44 years from 1980 to 2001. In addition, information on spouses/co-habitants (even those 44 and under) is included over the entire period. All in all, about 3.5 million persons are included. The database contains a large number of variables, including information on demographics, individuals’ labour market characteristics, financial aspects, transfer payments, and objective health measures. The health measures are merged in from the National Patient Registry, which has collected data on all 24-hour somatic hospital admissions since 1977 and since 1995, on part-time patients, out-patients and emergency patients as well. Information on 24-hour patients is used here. For each of these patient contacts, there is information on the hospital department admitting the patient, the diagnoses made, surgical procedures, date of admission, date of discharge and mode of admission (acute/non-acute). Health shocks are defined on the basis of diagnoses. In our data, diagnoses are classified according to the ICD-8 system before 1994 and according to the ICD-10 system after. In particular, we focus on hospitalization due to a particular type of new health shock since the last wave, namely acute health events (heart attack, stroke, new cancer). We also have access to and use information on the onset of any new chronic condition (arthritis, lung disease, diabetes, heart failure) since the last wave for which hospitalization takes place. An individual might be admitted to a hospital several times during a year. Moreover, more than one diagnosis might be attached to a particular admission. If more than one admission is recorded for an individual during a year, we focus on the first one. If more than one diagnosis is attached to this admission, we
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concentrate on the diagnosis which, according to the WHO’s international guidelines, can be characterized as the main condition. We restrict ourselves to a 20% random sample of individuals from the population registers who are 51-62 years of age in 1991 and are working in the previous wave. New persons are not added, so the sample becomes increasingly older but are never allowed to exceed age 64 at any time during the sample period, 65 being the age of eligibility of Medicare in the US. To match the sampling framework of the HRS, 5 two-year periods are constructed out of the 11 years of data at our disposal (1991-1993, 1993-1995 etc.). The sample restrictions leave 241,360 person-wave observations, split up by gender and marital status into 4 sub-samples, single men, single women, coupled men and coupled women. The analogous dataset for the US uses the first six waves, 1992-2002, of the Health and Retirement Study (HRS), 7 which is a national biennial panel survey of persons born between 1931 and 1941 and their spouses.8 Again, we use observations of single and coupled male and female workers who are between the ages of 51 and 62 in 1992, and are working in the previous wave. We only include HRS age-eligible individuals. The sample consists of five two-wave periods based on 6 waves of data (wave 1-2, wave 2-3, wave 3-4, etc) i.e. 18,847 person-wave observations. Instead of focusing narrowly on labor market exit through retirement, which is defined differently in the two datasets -- in the HRS retirement it is typically based on self-characterization as either fully, partially or not retired while in the Danish data, it is
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See Juster and Suzman (1995) and the HRS website at http://hrsonline.isr.umich.edu for an overview. The HRS is sponsored by the National Institute of Aging and conducted by the University of Michigan. We use the public use data files produced by the RAND Center for the Study of Aging (RAND HRS Data, Version F). The RAND HRS Data file is an easy to use longitudinal data set based on the HRS data, which was developed at RAND with funding from the National Institute on Aging and the Social Security Administration.
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based on the main source of income receipt -- we choose to operate with the widest definition of non-work as possible, including retirement, unemployment, disability, other type of benefit receipt (e.g. sickness absence) or being outside the labour market.9 Thus, our outcome measure is a dichotomous variable defined as working for pay or not. In the analyses for both countries, aside from the health shock measures, controls are added for the previous occurrence of an acute condition, previous experience of chronic illness, age dummies, education categories, industry/occupation indicators, year dummies and financial aspects.10 As far as possible, the same reference categories are defined in both countries. The construction of the two samples differs in two ways. First, as the Danish data from 2002 have only recently been added to the register sample and in need of further cleaning, for the time being, the Danish sample covers the period 1991-2001 while the US sample covers the period 1992-2002. Second, while the Danish health measures are objective medical diagnoses made at the time of hospital discharge available from the Danish National Patient Registry records and merged to the register sample, these measures are self-reported in the study for the US HRS. Subjective reports of health are prone to justification bias, see for example Anderson and Burkhauser (1985). However, individuals are probably less likely to misreport the presence and/or new diagnosis of a specific condition so that self-reported measures may serve as good proxies in this case. Yet, new findings by Baker et al. (2004) show considerable reporting error in self-reported objective measures as well. To reduce measurement error, we only count HRS self-reported 9
While an alternative could be an hours-based measure, actual hours of work is not present in the Danish registers. 10 That is, we include wealth but refrain from adding replacement rates, potential retirement benefit streams or other types of compensation measures as explanatory variables due to endogeneity concerns (Bound, 1989).
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diagnoses in a wave where individuals report at least an overnight hospital stay in the same wave except for the case of heart problems. Here, we count heart problems without a hospital stay as a chronic condition and those with an overnight hospital stay as an acute condition. We do not know whether the hospital stay was in conjunction with the reported diagnosis.11 On the other hand, objective health measures need not be correlated with work incapacity, see for example Bound (1991). In our analysis, we only consider serious conditions such as heart attack, stroke or new cancer which can be expected to impose work limitations. Working-for-pay rates are similar, but higher in the US. While 75.7% of the age-cohort is working for pay over this period in Denmark, the equivalent figure in the HRS is 82.9 (Appendix Table A.2.). In both countries, coupled men work the most, followed by single men, single women and coupled women. Appendices Table A.3. and A.4. show summary statistics for the background variables in the Danish and US sample respectively, where means are taken over all person-year observations. The samples match rather closely in age but some differences are present in educational attainment and industrial composition, suggesting that these factors should be controlled for in the estimation. While nearly 4% of the HRS sample experience an acute health event over this period, the corresponding figures in the Danish case is lower, at 1.7%. The incidence of new chronic conditions are the same in the two countries, but while 59% of Americans report having had an acute condition in the past, a full 32-46% report a past chronic condition; the corresponding numbers in the Danish case are about 3% and 7%. The large difference in the rates of past chronic conditions is most likely due 11
One problem is that for heart conditions in the first wave of the HRS, we are unable to identify whether a hospital stay occurred or not in connection with it, as the question is asked retrospectively. Therefore, we include these under chronic conditions.
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to either the self-reported nature of health in the HRS compared to the medically diagnosed health events present in the Danish data and/or that the US health system directs more resources towards preventive treatment and are more thorough in checking for existing conditions, so a variety of potential health problems (such as hypertension or high cholesterol) would be detected unlike in Denmark, where baseline health conditions are not routinely checked unless the patient has a problem.
IV. Empirical Model
We estimate pooled and random effects probability of work-for-pay models where in the latter, the latent variable WFPit* , the unobserved propensity to remain at work at time period t, given that the individual was working in time period t-1 is given by:
WFPit* = β 0 + β AN Acute _ newit + β CN Chronic _ new it + β X′ X it + α i + µi t
i = 1..N , t = 1..Ti
⎧1 if WFPit* > 0 , WFPit = ⎨ ⎩0 else
where WFPit is the observed working for pay indicator, Acute_new and Chronic_new measure the occurrence of any acute or chronic health shock between t-1 and t, and X is a vector of controls including any past acute shock, past chronic shock, age dummies, educational categories, industry/occupation indicators, year dummies and wealth. The individual effects α i are treated as random error terms assumed to be distributed independently of the regressors and with a normal distribution N (0, σ α2 ) . The idiosyncratic error µ it has a standard normal distribution N (0,1) . The likelihood
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function is calculated by Gauss-Hermite quadrature using xtprobit in STATA. If the composite error term vit = α i + µ it , then the correlation between any two error terms across periods vit and vis , is always the same, Corr( vit , vis ) = ρ =
σ α2 and is the σ α2 + σ µ2
fraction of variance attributable to σ α . The choice of random effects over fixed effects is meaningful here on two grounds: first, the fixed effects probit specification is not tractable due to the incidental parameters problem and second, fixed effects models in this set-up (e.g. logit fixed effects) would be identified only using information on individuals who changed working-for-pay status from one period to the next, excluding the majority of our young elderly sample who are still working. We also estimate pooled probit models as a benchmark. The pooled probit estimator should yield consistent estimates, although not as efficient as the random effects.
V. Results
Tables 1.a.-b. present the marginal effects12 of the pooled probit model with period effects and cluster-adjusted standard errors. According to the estimates from the Danish Longitudinal Register data in Table 1.a., a new acute health event (heart attack, stroke or new cancer) lowers older workers’ work for pay (WFP) probability by 6-7.5 pct. points depending on gender and family status, whereas the impact of a new chronic disease varies more across the sub-samples and lowers WFP chances by between 6.4-12 pct. points. Both sets of estimates are highly significant. In the equivalent US sample in Table 1.b., the corresponding estimates are also strongly significantly but different
12
We report the marginal effects at the means and for dummy variables for a change from 0 to 1.
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from Denmark for acute conditions where WFP is lowered by 17-23 pct. points. In fact, in terms of baseline probabilities, Americans are nearly three times as likely to stop working for pay following an acute health shock compared to Danes (21-28% of baseline probability in the US vs. 8-10% of baseline probability in Denmark). For new chronic conditions, the estimated impacts are more similar, a 6-12 pct. points reduction in WFP in Denmark compared to 6-18 pct. points fall in WFP in the US (8-16% of baseline probability in Denmark vs. 10-22% of baseline probability in the US). This is surprising because we match more narrowly on acute conditions rather than chronic conditions. In both countries, women, and especially married women, are more likely to reduce their WFP compared to men. Other parameter estimates conform to expectation. Past acute or past chronic conditions are also significant determinants of WFP but not as important as new health shocks and lower WFP by 3.5-7 pct. points in both countries. Older workers, the less educated, the non-self employed and the less wealthy are more likely to reduce labour supply following a wealth shock in both countries. Industry differences are more salient for WFP in the US. WFP rates drop sharply at the early retirement ages at both countries, age 60 in Denmark and age 62 in the US, although single men are most likely to stop working at this age in the US relative to the other groups, while in Denmark it is coupled women.
A. Health Insurance
To explore whether differences in health insurance systems can affect the country differences in WFP rates, we consider first only “insurance constrained” subsamples in the US (cf. Rust and Phelan, 1997) i.e. individuals who either have no health insurance or, have employer provided health insurance through their own or their
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spouse’s employment, but no retiree insurance through their own health plan (although some may have this type of coverage through their spouse’s health plan).13 Table 1.c. present the findings on the insurance constrained sample in the HRS data. The model could not be feasibly estimated on single men due to the small sample size. For the three other sub-samples, the results show that in the case of acute conditions, coupled men lacking insurance reduce work more compared to the full sample, but that compared to the full sample, the reduction in WFP rates is 4.6 pct. points less for single women and 2 pct. points less for coupled women. So, focusing only on insurance constrained individuals does narrow the country gap in WFP following an acute shock by 41% for single women and by about 23% for coupled women in terms of baseline probabilities but does not affect the country difference in impacts for coupled men. And, the marginal effects of an acute condition still remain considerably higher even in this insurance constrained sub-sample compared to the Danish case, between 15-23 pct. points (18-28% of baseline). Comparing the work response to a new chronic condition, single women reduce employment more in the insurance constrained sample compared to the full sample while coupled men reduce WFP by 3.4 pct. points less compared to the full sample. Our main hypothesis that individuals should retire to a greater extent in a universal health insurance system following a health shock is only partially supported by this evidence, as insurance constrained single women do tend to reduce their work effort significantly less following a new acute shock compared to the full sample of 13
The RAND HRS asks whether individuals are covered by health insurance from current or past employer (including union or self-employment), whether covered by spousal employer (including union or self-employment), whether employer provided HI covers retirees, whether covered by federal government HI program (including Medicaid, Medicare, CHAMPUS/VA, or other (waves 1-3)), whether covered by other HI, whether covered by LTC, whether covered by Life Insurance. So, information on whether individuals have access to retiree health insurance through their spouse’s plan is not present and neither do we know what part of the cost of the insurance is covered by the employer.
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single women as do insurance constrained coupled men, following a new chronic disease. The only other paper which has examined the effect of health insurance on the retirement decision of women considers only married women (Blau and Gilleskie, 2006). They find that the risk-reducing feature of health insurance explains only one tenth of the association between retiree health insurance and employment for this group. Our results on coupled women similarly show a more modest effect of having no health insurance on employment, i.e. coupled women without health insurance reduce workfor-pay 2.1 pct. points less following an acute health shock compared to the full sample of coupled women, and this is much less than the relative reduction we observed for single women. Part of the reason why we do not observe stronger effects of no health insurance on coupled women’s work decisions following health shocks may be that at least some individuals in this group would have access to retiree insurance through their spouse’s plan, which we are unable to ascertain in these data. Due to the limited sample size in this “insurance constrained” sub-sample, in Table 1.d., we return to the full HRS sample, but include indicators for, separately, no health insurance and employer provided health insurance but no (own) retiree insurance. These indicators are also interacted with health shock measures. The main effects are interesting as well. While not having any form of health insurance affects WFP for only some of the groups, increasing coupled men’s work for pay by 2.7 pct. points but reducing coupled women’s work for pay by 4.9 pct. points, having employer provided insurance but no retiree coverage increases WFP across the board between 3 and 6.6 pct. points where the reduction is, as expected, stronger for singles than couples. Interacting the insurance indicators with the health shock measures, however, hardly produces any significant effects, but a few large effects turn up – coupled men with no
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insurance are 17.7 pct. points less likely to work for pay following an acute shock and coupled men with no retiree insurance 8.3 pct. points less likely to do so, while single women with no retiree insurance are 8.5 pct. points more likely to work for pay following an acute shock. It is difficult to interpret the generated marginal effects on the interaction terms in non-linear models as the signs and significances can be misleading (Ai and Norton, 2002). Still, adding up the main effect of a new health shock with the interacted effect in the significant cases mentioned above, we find that single men reduce WFP by 17.5 pct. points after an acute shock which is a smaller reduction than what we found for the full sample without insurance variables in Table 1.b., while coupled men experience a large reduction in work effort of 30 pct. points following a new acute condition, considerably larger than in the specification without insurance indicators. For women, we find that while single women are 13 pct. points more likely to stop working for pay following an acute shock, which is a smaller reduction in work than what was found earlier in Table 1.b., married women are now 21 pct. points less likely to work for pay compared to 17.2 pct. points in Table 1.b. Computing the total effect of a new chronic illness, we find that while the effects are very close for coupled men and women in the model with insurance indicators and the model without, single men reduce their WFP by 6 pct. points less and single women 1.5 pct. points less in the model with insurance indicators following a new chronic illness. Still, in all cases, the main effect of a health shock dominates the interacted effect suggesting a lesser role for a lack of insurance in explaining the change in the working for pay probability after health shocks. But, in the specification with insurance dummies as well, we find that single women lacking insurance is the only group that
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significantly increases its work effort following an acute health shock, and further that it is those single women who have employer provided health insurance but no retiree coverage who are the ones most likely to work longer. For this group, we calculate that lack of complete insurance narrows the country difference in the impact of an acute condition by two-thirds. As the signs and significances of the marginal effects for interaction terms are uncertain, we hesitate to attach too much weight on this finding. In future work, however, we will compute the correct interaction effects and standard errors using the procedure suggested in the Ai and Norton (2002) study. As health insurance differences do not explain the differential response in work for pay following a health shock across the two countries, we turn to other potential explanations.
B. Unobserved Heterogeneity
In Tables 2.a. 2.b. and 2.c., the same models are re-estimated accounting for unobserved heterogeneity via random effects. Accounting for unobserved heterogeneity does not change the estimated impacts of a health shock in either Danish or US case. The marginal effect of an acute shock is still between 6.5-8.6 pct. points and the marginal effect of a chronic shock 6.6-13.5 pct. points in the Danish Longitudinal Registers, while the corresponding estimates are between 17.5-22.5 pct. points for an acute shock and between 8.4-18.5 pct. points for a chronic shock in the US HRS. Accounting for random effects in the specification with insurance indicators yields the same results as before, see Table 1.d. These results point to a limited role for unobserved heterogeneity in explaining the divergence in employment rates following a health shock in the two data sets. Although likelihood ratio tests fail to reject the
20
random effects estimator over the pooled probit estimator (log-likelihood values given at the bottom of Tables 1.a, 1.b., 1.d, 2.a., 2.b and 2.c.), the estimated parameters are virtually identical since the pooled probit is also consistent (though not efficient) and therefore, we retain the pooled probit specification with clustering at the individuallevel for all future analyses.
C. Justification Bias
A potential source of bias could arise due to the self-reported nature of health conditions in the US HRS data. Even though individuals are asked whether they have a specific condition from a set of illnesses that are typical for that age-group14, recent evidence by Baker, Stabile and Deri (2004) show considerable reporting error present in these socalled “objective” self-reported measures also. If individuals who stop working are more likely to report the presence of conditions to justify their leaving employment, this may induce a stronger correlation between health and employment compared to a situation where acute or chronic conditions (new or ever) are medically diagnosed at the time of hospitalization and not subject to individual mis-reporting. Currie and Madrian (1999) review a number of studies which try to address this potential source of endogeneity of SAH (self-assessed health) in labor models. To test whether the lower incidence of health shocks in the Danish sample drives the country difference in the estimates, we give US individuals the Danish rate of health shocks and predict their work-for-pay rates, assuming all other characteristics are fixed at the US levels. These results appear in Appendix Table A.5. For none of the sub-samples do the 95% confidence intervals (computed by the delta method) overlap suggesting that the
14
Hypertension, diabetes, cancer, lung disease, heart disease, arthritis and stroke
21
difference in predicted probabilities of work in the two cases is not large. That is, predicted WFP probabilities are not too different based on the lower Danish incidence of new or previous health shocks, compared to the US incidence of health shocks, although this assumes that the estimates are independent. Note that all our models tend to predict slightly lower work rates for individuals who have a low probability of working than what is observed.
D. Attrition and Sample Selection
We condition on working-for-pay in the previous wave, t-1, i.e. only individuals who are working at wave t-1 are selected and followed through until t. If individuals are not working in a particular wave but re-enter the ranks of the employed in a subsequent wave, they will be included. But, if workers drop out and stay out, they do not appear in the sample any longer. To test whether this sort of attrition from the sample over time could be biasing the estimates, we explore the effects of health disturbances arising in the first wave only in each case in Tables 3.A. and 3.B. The loss of degrees of freedom hinder the estimation for single men in the HRS and produce more noisy estimates in general, but it appears that acute health shocks cause a 3-7 pct. points reduction in work effort and chronic health shocks a 8-13 pct. points drop in Denmark, while they cause a corresponding 7-28 pct. points and 10-40 pct. points fall in employment in the US. So, if anything, the estimates are even more markedly different in the first possible wave indicating that attrition from the sample is not the reason for the divergence in estimates. A more fundamental type of selection takes place at the first wave itself, which may have different implications in the two cases. The multitude of early exit
22
programs in the Danish welfare state may tend to siphon out low wage/low SES workers from the labour market, a group for whom the replacement rate from early retirement pensions is high (see for example Bingley et al. 2004).15 Thus, the sample of employed workers 51-64 in the first wave in Denmark may be different and made up of predominantly high SES, white-collar workers with stronger tastes for work and in relatively better health and who are more prone to return to work following a health shock than in the US. For example, the medical literature shows that pre-existing comorbidities such as cardiac disease and poorer physical functioning – more prevalent among low SES groups (Marmot 2001) – are strongly related to worse work-related outcomes following heart attacks (McBurney et al. 2004). Among stroke patients, workers from white-collar occupations show a higher tendency to return to work (Saeki et al, 1995). In addition to early retirement programs, a generous and somewhat more easily accessible disability pension may imply that, older Danes, who began to develop health problems, withdrew themselves before the start of the sample period. So to start with, the sample may constitute a more selected group, health-wise, than comparable Americans.16 In a previous version of the paper, we had corrected for potential sample selection due to disability exit before sample start in the Danish case, by using a simple Heckman two-step procedure. The included variables were age, education and wealth
15
Denmark has a higher take-up of early retirement than the US. Although the normal age of retirement is 65 in Denmark, retirement at age 60 is widespread. Further, data from a Danish survey (Ældredatabasen) from 2002 show that among older workers almost 20% believe that remaining healthy will be an important aspect when they decide to retire. 16 Disability retirement is not as widely-used a path in the US where the majority of older individuals transit directly to receipt of SS benefits from full-time work at the early or normal ages. According to the Social Security Administration’s SS Bulletin, 1998, in the 50-54 age group, only 6% of men receive disability; at ages 55-59, this figure is 9% and at ages 60-64, 12.9% (Coile and Gruber, 2004). By way of comparison, in Denmark in 2000, 11.3% of the 5059 age group and 13.6% of the 60-64 age group retired through disability pension.
23
and the instrument we employed was the regional disability rate by age-group in 1991. That is, individuals living in regions with high disability rates in 1991 were themselves more likely to go on disability pension. Living in a high-disability region in 1991 was, however, assumed not to affect the individual’s subsequent working-for-pay decision and individuals were assumed not to move residence.17 Although the Mills ratio was significant, health shock effects remained unchanged when it was included as a regressor in the work model. In future work, we plan to estimate a panel selection model with correction for early retirement/disability take-up occurring throughout the sample period to test whether Danes may be stopping work at the first sign of a health problem, before any health shock occurs.
E. Robustness Checks
Tests of robustness are carried out on the basic pooled probit model (with insurance indicators in US) in Appendices A.6. and A.7. In terms of checks, we successively try imposing an income cutoff in Denmark (>50,000 D.Kr. annual income after tax in 2000-prices) and a hours cutoff >=20 in the US, a restriction in both countries to couples whose partner status remains unchanged throughout the sample period, the addition of more occupational categories in Denmark, a widening of the definition of work-for-pay to include the unemployed (both countries), restricting to whites only (US) and positive wealth (US). The results appear highly robust to these assumptions and show yet again, that workers in Denmark are about 6-11 pct. points less likely to continue working following a new acute condition and 6.3-13.6 pct. points less likely following a new chronic condition, while in the US, the equivalent estimates 17
Starting from age 50-54, residential mobility declines in European countries including Denmark (Tatsiramos, 2006)
24
range between 11-22 pct. points less and 6-17 pct. points less. All the robustness checks above were also performed on random effects probit models and the results remained the same.
VI. Discussion
We discuss some residual explanations which we are unable to test directly. One phenomenon occurring in US labor markets may be that older workers more easily leave their jobs following a health shock because re-entry to a bridge job at a lower wage and reduced hours is easier to obtain than in a high-wage, high-benefit labor market. Hutchens and Grace-Martin (2006) find, however, that most US employers are not willing to provide phased retirement opportunities for their workers (an exception being white collars), and that providing costly health insurance to such workers is part of the reason why. Retirement is more or less an absorbing state in Denmark so that conditional on working at that age, workers may be reluctant to give up their career jobs at the first opportunity. This aspect needs to be studied more. Or, differences in the health care production technology could be the driving factor. It is well known that the US medical system is more thorough and state-of-theart when it comes to treating acute conditions. Some evidence in this area is provided by Cutler and Mas (2006) who compare non-fatal health outcomes across U.S, Canada, U.K. and Spain and find that while the US medical system does worse in treating some chronic diseases such as diabetes compared to the other countries, it provides better acute care, particularly for heart diseases. In contrast, nationalized health care systems use dated technology and are notorious for short-staffed hospitals, where long waiting times and relatively short stays are typical for even acute care cases. If patients can
25
more easily be admitted, subject to more examinations and made to stay longer within the health care system, this may explain the greater propensity to be away from work in the US compared to Denmark following a health shock. The fact that the biggest difference in the estimated country impacts is evidenced for acute conditions suggests a role for these factors. We also do not test whether distinct selection mechanisms in play in the US draw out the relatively healthy workers from the labour market early, for example highincome individuals with adequate private pension savings which could explain the relatively stronger effect of health shocks on retirement in the US, given the relationship between morbidity and socioeconomic status discussed earlier. The available evidence in this area, however, suggests the opposite – that those with high discount rates, low assets and poor health retire at 62 (Gustman and Steinmeier, 2004). Related to this, is the potential problem of attrition from the US HRS data. If health-related attrition was present, however, it would bias the results in the opposite direction (see Appendix Table A.8. for evidence on attrition from our HRS sample). The difference in the findings may also be purely health-related. Older Americans might in general be in worse physical health than similar Danes (although they appear to be in better mental health), particularly where heart problems, high blood pressure and diabetes is concerned, see the evidence for men based on SHARE and HRS in Appendix Table A.9. The prevalence of obesity is higher in the US compared to Denmark (30 vs. 16 percent), see Michaud and van Soest (2005), and obesity is an important precursor to illness (Smith (2003), Goldman et al. (2004)). Differential health in the two samples might affect the results of our comparison. This is particularly the case if poor underlying health in terms of comorbidities and poorer physical functioning
26
make it more difficult to recover after an acute health event and thereby to return to work. To some extent these country differences in health may also reflect different racial and socioeconomic composition, as much previous work confirms the existence of a strong SES gradient in health as well as important health disparities in outcomes and access to health care across racial groups (Marmot, 1999, Keppel et al., 2004). A new study, however, comparing health of US and British heads of household using biological markers for diseases, finds that Americans have higher rates of diabetes, hypertension, heart disease, heart attack, stroke, lung disease and cancer than their English counterparts, and that these differences are apparently not related to differences in health behaviors (smoking, drinking etc.) and seem to be present at all points in the SES distribution (Banks et al. 2007). Redoing the US analysis on whites only and those with positive wealth made no difference to our results.
VII. Conclusions
This paper compares the effect of acute health shocks and new chronic conditions on the probability of working for pay among sub-samples of elderly workers from the Danish Longitudinal Registers, 1991-2001, to that found in similarly defined sub-samples from the US HRS over the same period. We expected that health shocks would reduce work to a greater extent in a general tax-financed health-care system with universal access such as the Danish one, compared to the US system. Our results show that while new chronic conditions lead individuals to reduce work at about the same rate in both countries, older Americans are three times as likely to stop working for pay following an acute condition than similar Danes. Accounting for insurance differences narrows
27
this gap by almost two-thirds for single women who suffer an acute shock and lack retiree insurance but do not affect the findings for any other group. Thus, insurance differences cannot explain this puzzling gap which persists even after accounting for unobserved heterogeneity and justification bias in self-assessed health and which, furthermore, is robust to different checks. In future work we will estimate panel selection models which will take into account eligibility to various early exit programs which may be drawing out low wage/low SES workers with weaker work preferences and poorer general health in Denmark. Some residual explanations may be more difficult to test: Getting a new job may be easier for older workers in the US; The US healthcare system may be more aggressive in treating and retaining acute care cases within the health system; Danes could be in better general health than their American counterparts and therefore less debilitated by the impact of a health shock. Indeed, obesity (an important precursor of illness) as well as the incidence of ischemic heart diseases is significantly lower among older Danes. On the face of it, there seems to be little evidence to suggest that a universally-insured health-care system like the Danish one leads to excessive retirement following a health shock, although there may be a hidden selection in that Danes may tend to retire at the first sign of a health problem, before a health shock can occur.
28
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Tables Table 1.a. Pooled Probit, Marginal Effects on Working for Pay by Gender and Family Status, Danish Longitudinal Registers, 1991-2001 Single Single Coupled Coupled Men Women Men Women New acute event -0.061** -0.074*** -0.067*** -0.075*** (0.026) (0.024) (0.010) (0.014) New chronic condition -0.081*** -0.123*** -0.064*** -0.115*** (0.020) (0.020) (0.008) (0.013) Ever Acute event -0.040** -0.008 -0.047*** -0.049*** (0.020) (0.016) (0.007) (0.010) Ever Chronic condition -0.033*** -0.073*** -0.035*** -0.039*** (0.012) (0.013) (0.005) (0.007) age=52 0.001 -0.041 -0.028** -0.035** (0.023) (0.026) (0.011) (0.014) age=53 -0.004 -0.050** -0.021** -0.061*** (0.020) (0.023) (0.010) (0.012) age=54 -0.011 -0.051** -0.043*** -0.088*** (0.021) (0.024) (0.011) (0.013) age=55 -0.028 -0.054** -0.043*** -0.095*** (0.020) (0.023) (0.010) (0.012) age=56 -0.043** -0.071*** -0.071*** -0.099*** (0.021) (0.023) (0.011) (0.012) age=57 -0.046** -0.079*** -0.079*** -0.104*** (0.021) (0.023) (0.010) (0.012) age=58 -0.209*** -0.293*** -0.308*** -0.366*** (0.024) (0.025) (0.012) (0.012) age=59 -0.294*** -0.377*** -0.420*** -0.490*** (0.024) (0.023) (0.012) (0.011) age=60 -0.254*** -0.325*** -0.396*** -0.430*** (0.025) (0.025) (0.012) (0.012) age=61 -0.256*** -0.382*** -0.432*** -0.411*** (0.026) (0.025) (0.013) (0.013) age=62 -0.266*** -0.426*** -0.457*** -0.430*** (0.026) (0.024) (0.013) (0.013) Vocational education -0.002 0.045*** 0.001 0.039*** (0.007) (0.007) (0.003) (0.004) Short education 0.031 0.078*** 0.050*** 0.103*** (0.019) (0.014) (0.006) (0.008) Medium education 0.086*** 0.108*** 0.079*** 0.112*** (0.009) (0.007) (0.003) (0.004) Long education and 0.147*** 0.190*** 0.133*** 0.182*** above (0.009) (0.009) (0.003) (0.007) Period 1993-1995 0.036*** 0.003 0.015*** -0.020*** (0.008) (0.008) (0.003) (0.005) Period 1995-1997 0.059*** 0.041*** 0.044*** 0.037***
35
Period 1997-1999 Period 1999-2001 Self-employed Agriculture, construction Manufacturing Trade Transportation Public sector Missing industry Log wealth/20, D.Kr., 2000-prices Ll chi2 N
Single Men (0.009) 0.069*** (0.009) 0.083*** (0.010) 0.071*** (0.015) 0.006 (0.013) 0.024* (0.013) 0.013 (0.012) 0.018 (0.015) -0.007 (0.011) 0.084*** (0.016) 0.105*** (0.012) -10677.38 1382.298 20,677
Single Women (0.009) 0.047*** (0.009) 0.070*** (0.010) 0.020 (0.018) 0.021 (0.013) 0.008 (0.014) -0.004 (0.013) 0.013 (0.018) 0.015 (0.010) 0.057*** (0.015) 0.024** (0.012) -13683.476 2218.013 25,430
Coupled Men (0.003) 0.040*** (0.004) 0.057*** (0.004) 0.070*** (0.005) 0.001 (0.005) -0.004 (0.005) -0.006 (0.005) -0.005 (0.006) 0.003 (0.004) 0.045*** (0.006) 0.038*** (0.005) -52682.122 12051.196 117,515
Coupled Women (0.005) 0.019*** (0.006) 0.039*** (0.007) 0.038*** (0.009) 0.012 (0.008) 0.002 (0.008) 0.004 (0.008) 0.018* (0.010) 0.016** (0.006) 0.053*** (0.010) 0.006 (0.007) -41681.937 9094.439 77,738
Marginal effects; Standard errors in parentheses. For discrete change of dummy variables from 0 to 1. * p
